GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute
It addresses access control for digital healthcare, offering a personalized solution that meets compliance and adaptability needs, though it appears incremental as it builds on existing components like GPT and CAABAC.
This study tackled the problem of securing electronic health records by developing the GPT-Onto-CAABAC framework, which integrates GPT, ontologies, and context-aware access control to dynamically interpret policies and adapt to changing environments, resulting in improved security through accurate alignment with regulatory requirements.
As digital healthcare evolves, the security of electronic health records (EHR) becomes increasingly crucial. This study presents the GPT-Onto-CAABAC framework, integrating Generative Pretrained Transformer (GPT), medical-legal ontologies and Context-Aware Attribute-Based Access Control (CAABAC) to enhance EHR access security. Unlike traditional models, GPT-Onto-CAABAC dynamically interprets policies and adapts to changing healthcare and legal environments, offering customized access control solutions. Through empirical evaluation, this framework is shown to be effective in improving EHR security by accurately aligning access decisions with complex regulatory and situational requirements. The findings suggest its broader applicability in sectors where access control must meet stringent compliance and adaptability standards.